CASIA OpenIR  > 09年以前成果
Recursive support vector machines for dimensionality reduction
Tao, Qing1,2; Chu, Dejun2; Wang, Jue1
2008
发表期刊IEEE TRANSACTIONS ON NEURAL NETWORKS
卷号19期号:1页码:189-193
文章类型Article
摘要The usual dimensionality reduction technique in supervised learning is mainly based on linear discriminant analysis (LDA), but it suffers from singularity or undersampled problems. On the other hand, a regular support vector machine (SVM) separates the data only in terms of one single direction of maximum margin, and the classification accuracy may be not good enough. In this letter, a recursive SVM (RSVM) is presented, in which several orthogonal directions that best separate the data with the maximum margin are obtained. Theoretical analysis shows that a completely orthogonal basis can be derived in feature subspace spanned by the training samples and the margin is decreasing along the recursive components in linearly separable cases. As a result, a new dimensionality reduction technique based on multilevel maximum margin components and then a classifier with high accuracy are achieved. Experiments in synthetic and several real data sets show that RSVM using multilevel maximum margin features can do efficient dimensionality reduction and outperform regular SVM in binary classification problems.
关键词Classification Dimensionality Reduction Feature Extraction Projection Recursive Support Vector Machines (Rsvms) Support Vector Machines (Svms).
WOS标题词Science & Technology ; Technology
关键词[WOS]FISHER LINEAR DISCRIMINANT ; FACE RECOGNITION ; THEORETICAL-ANALYSIS
收录类别SCI
语种英语
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号WOS:000252516700017
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/9639
专题09年以前成果
作者单位1.Chinese Acad Sci, Inst Automat, Key Lab Complex Syst & Intelligence Sci, Beijing 100080, Peoples R China
2.New Star Res Inst Appl Technol, Hefei 230031, Peoples R China
推荐引用方式
GB/T 7714
Tao, Qing,Chu, Dejun,Wang, Jue. Recursive support vector machines for dimensionality reduction[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS,2008,19(1):189-193.
APA Tao, Qing,Chu, Dejun,&Wang, Jue.(2008).Recursive support vector machines for dimensionality reduction.IEEE TRANSACTIONS ON NEURAL NETWORKS,19(1),189-193.
MLA Tao, Qing,et al."Recursive support vector machines for dimensionality reduction".IEEE TRANSACTIONS ON NEURAL NETWORKS 19.1(2008):189-193.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Tao, Qing]的文章
[Chu, Dejun]的文章
[Wang, Jue]的文章
百度学术
百度学术中相似的文章
[Tao, Qing]的文章
[Chu, Dejun]的文章
[Wang, Jue]的文章
必应学术
必应学术中相似的文章
[Tao, Qing]的文章
[Chu, Dejun]的文章
[Wang, Jue]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。